Our proposed network is closer to what is referred to as "reservoir computing" ([1]}), in that it acts as a chaotic, dynamic system due to the neuron update, lateral connections, and randomness placed in the network. Reservoir computers are a type of recurrent neural network that do not learn on the recurrent connections within the random recurrent connections but rather only on a read-out layer. Reservoir computers have demonstrated high success in classification of difficult temporal patterns due to the integration of time information in each artificial neuron's update rule. They have been shown to encode very complicated temporal input, even input that exhibits very chaotic patterns such as the Lorentz attractor. Although reservoir computers require a strong supervised signal to drive them towards a target, they still demonstrate that random weights and connections are a powerful factor in a network's ability to encode temporal data. As such, they have been successfully used to analyze complicated recurrent neuronal dynamics in decision making ([2]}, [3]}).
